Report on public perceptions in cross-country survey
Bibliographic record
Abstract
This deliverable synthesizes the first results on public perceptions of marine Carbon Dioxide Removal (CDR) methods from a cross-country survey in Canada, China, France, Germany, Norway, and Taiwan. The purpose is to inform the other work packages in OceanNets and stakeholders about our results in a timely and brief manner about the ways members of the public view marine CDR specifically. The survey was fielded in April 2023, has approximately 2000 observations in each country, and aims to be representative for the population active online in the respective country. It covers the marine CDR approaches ocean alkalinity enhancement (OAE), macroalgae farming with BECCS (mBECCS) or macroalgae farming with biomass sinking. Our analysis found notable differences in perceptions of the three methods and between the countries. OAE received the largest shares of negative assessments in all countries, mBECCS received the highest shares of positive assessments. Overall, respondents in the Asian countries assess ocean-based CDR approaches more positively than respondents in Western countries. We also find differences in self-reported familiarity. In Western countries, a majority (55-84%) report never having heard of these approaches; in Asian countries, a majority (56-75%) report having heard of the approaches before. Results on the associations with the methods confirm the results for the general question and add more nuanced insights into how the methods are perceived. The survey also included an experimental design that indicates a potential spillover effect, wherein presenting OAE first negatively influenced perceptions of the subsequent technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".